Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations282
Missing cells712
Missing cells (%)11.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory226.6 KiB
Average record size in memory822.9 B

Variable types

Numeric8
Text4
Unsupported2
URL1
Categorical5
DateTime2

Alerts

DIRECTION is highly overall correlated with ID and 2 other fieldsHigh correlation
DISTRICT is highly overall correlated with DISTRICT.1 and 5 other fieldsHigh correlation
DISTRICT.1 is highly overall correlated with DISTRICT and 6 other fieldsHigh correlation
ID is highly overall correlated with DIRECTION and 4 other fieldsHigh correlation
LAT is highly overall correlated with DISTRICT and 3 other fieldsHigh correlation
LONG is highly overall correlated with DISTRICT and 3 other fieldsHigh correlation
NEIGHBOURHOOD is highly overall correlated with DISTRICT and 6 other fieldsHigh correlation
SEQ NUMBER is highly overall correlated with DIRECTION and 2 other fieldsHigh correlation
STATUS is highly overall correlated with DIRECTIONHigh correlation
X is highly overall correlated with DISTRICT and 3 other fieldsHigh correlation
Y is highly overall correlated with DISTRICT and 3 other fieldsHigh correlation
YEAR_PLACEMENT is highly overall correlated with ID and 1 other fieldsHigh correlation
DIRECTION is highly imbalanced (59.9%) Imbalance
STATUS is highly imbalanced (96.6%) Imbalance
DESCRIPTION has 282 (100.0%) missing values Missing
TEXT_ON_THE_STONE has 282 (100.0%) missing values Missing
YEAR_PLACEMENT has 12 (4.3%) missing values Missing
BWT_NUMMER has 13 (4.6%) missing values Missing
NEIGHBOURHOOD has 61 (21.6%) missing values Missing
DISTRICT.1 has 61 (21.6%) missing values Missing
SEQ NUMBER is uniformly distributed Uniform
ID has unique values Unique
SEQ NUMBER has unique values Unique
PHOTO FILE has unique values Unique
DESCRIPTION is an unsupported type, check if it needs cleaning or further analysis Unsupported
TEXT_ON_THE_STONE is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2025-09-09 13:37:45.372331
Analysis finished2025-09-09 13:37:50.281405
Duration4.91 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

ID
Real number (ℝ)

High correlation  Unique 

Distinct282
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6097584.9
Minimum6009832
Maximum6905016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2025-09-09T16:37:50.342169image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum6009832
5-th percentile6009846
Q16009902.2
median6009973.5
Q36010044.8
95-th percentile6905002
Maximum6905016
Range895184
Interquartile range (IQR)142.5

Descriptive statistics

Standard deviation249702.09
Coefficient of variation (CV)0.040950983
Kurtosis6.519764
Mean6097584.9
Median Absolute Deviation (MAD)71.5
Skewness2.8756472
Sum1.7195189 × 109
Variance6.2351136 × 1010
MonotonicityNot monotonic
2025-09-09T16:37:50.434373image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6905016 1
 
0.4%
6009918 1
 
0.4%
6009919 1
 
0.4%
6009920 1
 
0.4%
6009921 1
 
0.4%
6009922 1
 
0.4%
6009923 1
 
0.4%
6009924 1
 
0.4%
6009925 1
 
0.4%
6009926 1
 
0.4%
Other values (272) 272
96.5%
ValueCountFrequency (%)
6009832 1
0.4%
6009833 1
0.4%
6009834 1
0.4%
6009835 1
0.4%
6009836 1
0.4%
6009837 1
0.4%
6009838 1
0.4%
6009839 1
0.4%
6009840 1
0.4%
6009841 1
0.4%
ValueCountFrequency (%)
6905016 1
0.4%
6905015 1
0.4%
6905014 1
0.4%
6905013 1
0.4%
6905012 1
0.4%
6905011 1
0.4%
6905010 1
0.4%
6905009 1
0.4%
6905008 1
0.4%
6905007 1
0.4%
Distinct193
Distinct (%)68.4%
Missing0
Missing (%)0.0%
Memory size20.3 KiB
2025-09-09T16:37:50.572635image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length28
Median length22
Mean length15.241135
Min length4

Characters and Unicode

Total characters4298
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique140 ?
Unique (%)49.6%

Sample

1st rowJuliana van Stolberglaan
2nd rowJuliana van Stolberglaan
3rd rowKeizerstraat
4th rowKornoeljestraat
5th rowKortrijksestraat
ValueCountFrequency (%)
van 55
 
12.9%
laan 8
 
1.9%
de 7
 
1.6%
zwetstraat 6
 
1.4%
der 6
 
1.4%
jan 6
 
1.4%
goudsbloemlaan 5
 
1.2%
pletterijstraat 5
 
1.2%
weteringkade 5
 
1.2%
stevinstraat 4
 
0.9%
Other values (226) 320
74.9%
2025-09-09T16:37:50.801540image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 709
16.5%
t 479
11.1%
e 424
 
9.9%
r 368
 
8.6%
s 319
 
7.4%
n 313
 
7.3%
i 170
 
4.0%
l 164
 
3.8%
145
 
3.4%
o 135
 
3.1%
Other values (44) 1072
24.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4298
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 709
16.5%
t 479
11.1%
e 424
 
9.9%
r 368
 
8.6%
s 319
 
7.4%
n 313
 
7.3%
i 170
 
4.0%
l 164
 
3.8%
145
 
3.4%
o 135
 
3.1%
Other values (44) 1072
24.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4298
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 709
16.5%
t 479
11.1%
e 424
 
9.9%
r 368
 
8.6%
s 319
 
7.4%
n 313
 
7.3%
i 170
 
4.0%
l 164
 
3.8%
145
 
3.4%
o 135
 
3.1%
Other values (44) 1072
24.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4298
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 709
16.5%
t 479
11.1%
e 424
 
9.9%
r 368
 
8.6%
s 319
 
7.4%
n 313
 
7.3%
i 170
 
4.0%
l 164
 
3.8%
145
 
3.4%
o 135
 
3.1%
Other values (44) 1072
24.9%

SEQ NUMBER
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct282
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean143.16667
Minimum1
Maximum285
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2025-09-09T16:37:50.892071image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15.05
Q172.25
median143.5
Q3214.75
95-th percentile270.95
Maximum285
Range284
Interquartile range (IQR)142.5

Descriptive statistics

Standard deviation82.657058
Coefficient of variation (CV)0.57734848
Kurtosis-1.2049737
Mean143.16667
Median Absolute Deviation (MAD)71.5
Skewness-0.0043026319
Sum40373
Variance6832.1892
MonotonicityNot monotonic
2025-09-09T16:37:50.984954image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
285 1
 
0.4%
88 1
 
0.4%
89 1
 
0.4%
90 1
 
0.4%
91 1
 
0.4%
92 1
 
0.4%
93 1
 
0.4%
94 1
 
0.4%
95 1
 
0.4%
96 1
 
0.4%
Other values (272) 272
96.5%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
3 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
ValueCountFrequency (%)
285 1
0.4%
284 1
0.4%
283 1
0.4%
282 1
0.4%
281 1
0.4%
280 1
0.4%
279 1
0.4%
278 1
0.4%
277 1
0.4%
276 1
0.4%
Distinct164
Distinct (%)58.2%
Missing0
Missing (%)0.0%
Memory size16.5 KiB
2025-09-09T16:37:51.184100image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.3439716
Min length1

Characters and Unicode

Total characters661
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique99 ?
Unique (%)35.1%

Sample

1st row352
2nd row368
3rd row65
4th row4
5th row41
ValueCountFrequency (%)
8 6
 
2.1%
3 6
 
2.1%
55 5
 
1.8%
41 5
 
1.8%
26 5
 
1.8%
95 4
 
1.4%
5 4
 
1.4%
33 4
 
1.4%
61 4
 
1.4%
29 4
 
1.4%
Other values (154) 237
83.5%
2025-09-09T16:37:51.469774image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 128
19.4%
2 93
14.1%
3 70
10.6%
6 68
10.3%
5 67
10.1%
4 56
8.5%
0 44
 
6.7%
7 43
 
6.5%
9 37
 
5.6%
8 28
 
4.2%
Other values (7) 27
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 128
19.4%
2 93
14.1%
3 70
10.6%
6 68
10.3%
5 67
10.1%
4 56
8.5%
0 44
 
6.7%
7 43
 
6.5%
9 37
 
5.6%
8 28
 
4.2%
Other values (7) 27
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 128
19.4%
2 93
14.1%
3 70
10.6%
6 68
10.3%
5 67
10.1%
4 56
8.5%
0 44
 
6.7%
7 43
 
6.5%
9 37
 
5.6%
8 28
 
4.2%
Other values (7) 27
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 128
19.4%
2 93
14.1%
3 70
10.6%
6 68
10.3%
5 67
10.1%
4 56
8.5%
0 44
 
6.7%
7 43
 
6.5%
9 37
 
5.6%
8 28
 
4.2%
Other values (7) 27
 
4.1%

DESCRIPTION
Unsupported

Missing  Rejected  Unsupported 

Missing282
Missing (%)100.0%
Memory size2.3 KiB

PHOTO FILE
URL

Unique 

Distinct282
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size31.4 KiB
https://denhaag.gisib.nl/fotos/Stolpersteine/Foto/285.jpg
 
1
https://denhaag.gisib.nl/fotos/Stolpersteine/Foto/88.jpg
 
1
https://denhaag.gisib.nl/fotos/Stolpersteine/Foto/89.jpg
 
1
https://denhaag.gisib.nl/fotos/Stolpersteine/Foto/90.jpg
 
1
https://denhaag.gisib.nl/fotos/Stolpersteine/Foto/91.jpg
 
1
Other values (277)
277 
ValueCountFrequency (%)
https://denhaag.gisib.nl/fotos/Stolpersteine/Foto/285.jpg 1
 
0.4%
https://denhaag.gisib.nl/fotos/Stolpersteine/Foto/88.jpg 1
 
0.4%
https://denhaag.gisib.nl/fotos/Stolpersteine/Foto/89.jpg 1
 
0.4%
https://denhaag.gisib.nl/fotos/Stolpersteine/Foto/90.jpg 1
 
0.4%
https://denhaag.gisib.nl/fotos/Stolpersteine/Foto/91.jpg 1
 
0.4%
https://denhaag.gisib.nl/fotos/Stolpersteine/Foto/92.jpg 1
 
0.4%
https://denhaag.gisib.nl/fotos/Stolpersteine/Foto/93.jpg 1
 
0.4%
https://denhaag.gisib.nl/fotos/Stolpersteine/Foto/94.jpg 1
 
0.4%
https://denhaag.gisib.nl/fotos/Stolpersteine/Foto/95.jpg 1
 
0.4%
https://denhaag.gisib.nl/fotos/Stolpersteine/Foto/96.jpg 1
 
0.4%
Other values (272) 272
96.5%
ValueCountFrequency (%)
https 282
100.0%
ValueCountFrequency (%)
denhaag.gisib.nl 282
100.0%
ValueCountFrequency (%)
/fotos/Stolpersteine/Foto/285.jpg 1
 
0.4%
/fotos/Stolpersteine/Foto/88.jpg 1
 
0.4%
/fotos/Stolpersteine/Foto/89.jpg 1
 
0.4%
/fotos/Stolpersteine/Foto/90.jpg 1
 
0.4%
/fotos/Stolpersteine/Foto/91.jpg 1
 
0.4%
/fotos/Stolpersteine/Foto/92.jpg 1
 
0.4%
/fotos/Stolpersteine/Foto/93.jpg 1
 
0.4%
/fotos/Stolpersteine/Foto/94.jpg 1
 
0.4%
/fotos/Stolpersteine/Foto/95.jpg 1
 
0.4%
/fotos/Stolpersteine/Foto/96.jpg 1
 
0.4%
Other values (272) 272
96.5%
ValueCountFrequency (%)
282
100.0%
ValueCountFrequency (%)
282
100.0%

TEXT_ON_THE_STONE
Unsupported

Missing  Rejected  Unsupported 

Missing282
Missing (%)100.0%
Memory size2.3 KiB
Distinct269
Distinct (%)95.4%
Missing0
Missing (%)0.0%
Memory size26.0 KiB
2025-09-09T16:37:51.600460image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length109
Median length48
Mean length33.166667
Min length9

Characters and Unicode

Total characters9353
Distinct characters77
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique257 ?
Unique (%)91.1%

Sample

1st rowBenjamin van Dantzig (joodsmonument.nl)
2nd rowGerson de Levie (joodsmonument.nl)
3rd rowFrederik Alter (joodsmonument.nl)
4th rowJudica Mozes (joodsmonument.nl)
5th rowHans Paradies (joodsmonument.nl)
ValueCountFrequency (%)
joodsmonument.nl 187
 
18.6%
van 49
 
4.9%
de 24
 
2.4%
16
 
1.6%
oorlogsgravenstichting 11
 
1.1%
joseph 9
 
0.9%
benjamin 8
 
0.8%
cohen 8
 
0.8%
mozes 8
 
0.8%
salomon 8
 
0.8%
Other values (454) 677
67.4%
2025-09-09T16:37:51.828222image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 987
 
10.6%
o 855
 
9.1%
e 846
 
9.0%
723
 
7.7%
a 488
 
5.2%
m 480
 
5.1%
s 446
 
4.8%
l 406
 
4.3%
r 393
 
4.2%
t 385
 
4.1%
Other values (67) 3344
35.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9353
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 987
 
10.6%
o 855
 
9.1%
e 846
 
9.0%
723
 
7.7%
a 488
 
5.2%
m 480
 
5.1%
s 446
 
4.8%
l 406
 
4.3%
r 393
 
4.2%
t 385
 
4.1%
Other values (67) 3344
35.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9353
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 987
 
10.6%
o 855
 
9.1%
e 846
 
9.0%
723
 
7.7%
a 488
 
5.2%
m 480
 
5.1%
s 446
 
4.8%
l 406
 
4.3%
r 393
 
4.2%
t 385
 
4.1%
Other values (67) 3344
35.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9353
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 987
 
10.6%
o 855
 
9.1%
e 846
 
9.0%
723
 
7.7%
a 488
 
5.2%
m 480
 
5.1%
s 446
 
4.8%
l 406
 
4.3%
r 393
 
4.2%
t 385
 
4.1%
Other values (67) 3344
35.8%

NUMBER
Real number (ℝ)

Distinct9
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1985816
Minimum0
Maximum8
Zeros2
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2025-09-09T16:37:51.902571image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4720159
Coefficient of variation (CV)0.6695298
Kurtosis1.896067
Mean2.1985816
Median Absolute Deviation (MAD)1
Skewness1.3788318
Sum620
Variance2.1668307
MonotonicityNot monotonic
2025-09-09T16:37:51.981671image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 121
42.9%
2 69
24.5%
3 36
 
12.8%
4 34
 
12.1%
5 11
 
3.9%
7 4
 
1.4%
6 3
 
1.1%
8 2
 
0.7%
0 2
 
0.7%
ValueCountFrequency (%)
0 2
 
0.7%
1 121
42.9%
2 69
24.5%
3 36
 
12.8%
4 34
 
12.1%
5 11
 
3.9%
6 3
 
1.1%
7 4
 
1.4%
8 2
 
0.7%
ValueCountFrequency (%)
8 2
 
0.7%
7 4
 
1.4%
6 3
 
1.1%
5 11
 
3.9%
4 34
 
12.1%
3 36
 
12.8%
2 69
24.5%
1 121
42.9%
0 2
 
0.7%

YEAR_PLACEMENT
Real number (ℝ)

High correlation  Missing 

Distinct10
Distinct (%)3.7%
Missing12
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean2021.5519
Minimum2013
Maximum2025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2025-09-09T16:37:52.060132image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2013
5-th percentile2016
Q12019
median2022
Q32024
95-th percentile2025
Maximum2025
Range12
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8758539
Coefficient of variation (CV)0.0014225971
Kurtosis-0.0090702905
Mean2021.5519
Median Absolute Deviation (MAD)2
Skewness-0.94345249
Sum545819
Variance8.2705356
MonotonicityNot monotonic
2025-09-09T16:37:52.134402image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2022 79
28.0%
2024 64
22.7%
2023 27
 
9.6%
2025 24
 
8.5%
2019 23
 
8.2%
2018 19
 
6.7%
2017 17
 
6.0%
2016 9
 
3.2%
2013 4
 
1.4%
2015 4
 
1.4%
(Missing) 12
 
4.3%
ValueCountFrequency (%)
2013 4
 
1.4%
2015 4
 
1.4%
2016 9
 
3.2%
2017 17
 
6.0%
2018 19
 
6.7%
2019 23
 
8.2%
2022 79
28.0%
2023 27
 
9.6%
2024 64
22.7%
2025 24
 
8.5%
ValueCountFrequency (%)
2025 24
 
8.5%
2024 64
22.7%
2023 27
 
9.6%
2022 79
28.0%
2019 23
 
8.2%
2018 19
 
6.7%
2017 17
 
6.0%
2016 9
 
3.2%
2015 4
 
1.4%
2013 4
 
1.4%

BWT_NUMMER
Text

Missing 

Distinct243
Distinct (%)90.3%
Missing13
Missing (%)4.6%
Memory size18.9 KiB
2025-09-09T16:37:52.256911image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length15
Median length14
Mean length13.063197
Min length1

Characters and Unicode

Total characters3514
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique235 ?
Unique (%)87.4%

Sample

1st rowBWT-01373GGB12
2nd rowBWT-00109GGB13
3rd rowBWT-00185GGB17
4th rowBWT-00274GGB20
5th rowBWT-00299GGB19
ValueCountFrequency (%)
18
 
6.7%
bwt-00637ggb16 3
 
1.1%
bwt-00658ggb16 3
 
1.1%
bwt-00169ggb17 2
 
0.7%
bwt-00640ggb16 2
 
0.7%
bwt-00840ggb19 2
 
0.7%
bwt-00196ggb17 2
 
0.7%
bwt-00050ggb19 2
 
0.7%
bwt-00732ggb18 1
 
0.4%
bwt-00274ggb20 1
 
0.4%
Other values (233) 233
86.6%
2025-09-09T16:37:52.552069image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
B 499
14.2%
G 498
14.2%
0 470
13.4%
2 339
9.6%
1 282
8.0%
- 269
7.7%
T 251
7.1%
W 251
7.1%
6 118
 
3.4%
3 113
 
3.2%
Other values (6) 424
12.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3514
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 499
14.2%
G 498
14.2%
0 470
13.4%
2 339
9.6%
1 282
8.0%
- 269
7.7%
T 251
7.1%
W 251
7.1%
6 118
 
3.4%
3 113
 
3.2%
Other values (6) 424
12.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3514
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 499
14.2%
G 498
14.2%
0 470
13.4%
2 339
9.6%
1 282
8.0%
- 269
7.7%
T 251
7.1%
W 251
7.1%
6 118
 
3.4%
3 113
 
3.2%
Other values (6) 424
12.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3514
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 499
14.2%
G 498
14.2%
0 470
13.4%
2 339
9.6%
1 282
8.0%
- 269
7.7%
T 251
7.1%
W 251
7.1%
6 118
 
3.4%
3 113
 
3.2%
Other values (6) 424
12.1%

DIRECTION
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)1.1%
Missing1
Missing (%)0.4%
Memory size17.2 KiB
Haaks
242 
haaks
36 
Linksom
 
3

Length

Max length7
Median length5
Mean length5.0213523
Min length5

Characters and Unicode

Total characters1411
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHaaks
2nd rowHaaks
3rd rowHaaks
4th rowHaaks
5th rowHaaks

Common Values

ValueCountFrequency (%)
Haaks 242
85.8%
haaks 36
 
12.8%
Linksom 3
 
1.1%
(Missing) 1
 
0.4%

Length

2025-09-09T16:37:52.648024image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-09T16:37:52.724435image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
haaks 278
98.9%
linksom 3
 
1.1%

Most occurring characters

ValueCountFrequency (%)
a 556
39.4%
k 281
19.9%
s 281
19.9%
H 242
17.2%
h 36
 
2.6%
L 3
 
0.2%
i 3
 
0.2%
n 3
 
0.2%
o 3
 
0.2%
m 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1411
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 556
39.4%
k 281
19.9%
s 281
19.9%
H 242
17.2%
h 36
 
2.6%
L 3
 
0.2%
i 3
 
0.2%
n 3
 
0.2%
o 3
 
0.2%
m 3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1411
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 556
39.4%
k 281
19.9%
s 281
19.9%
H 242
17.2%
h 36
 
2.6%
L 3
 
0.2%
i 3
 
0.2%
n 3
 
0.2%
o 3
 
0.2%
m 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1411
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 556
39.4%
k 281
19.9%
s 281
19.9%
H 242
17.2%
h 36
 
2.6%
L 3
 
0.2%
i 3
 
0.2%
n 3
 
0.2%
o 3
 
0.2%
m 3
 
0.2%
Distinct8
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
Minimum2023-11-01 00:00:00
Maximum2025-04-24 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-09-09T16:37:52.789055image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:52.857204image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)

NEIGHBOURHOOD
Categorical

High correlation  Missing 

Distinct46
Distinct (%)20.8%
Missing61
Missing (%)21.6%
Memory size19.0 KiB
Belgisch Park
25 
Bezuidenhout-oost
25 
Statenkwartier
20 
Bezuidenhout-midden
16 
Rivierenbuurt-noord
13 
Other values (41)
122 

Length

Max length23
Median length20
Mean length15.076923
Min length6

Characters and Unicode

Total characters3332
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)6.8%

Sample

1st rowBezuidenhout-oost
2nd rowBezuidenhout-oost
3rd rowScheveningen Badplaats
4th rowEykenduinen
5th rowBelgisch Park

Common Values

ValueCountFrequency (%)
Belgisch Park 25
 
8.9%
Bezuidenhout-oost 25
 
8.9%
Statenkwartier 20
 
7.1%
Bezuidenhout-midden 16
 
5.7%
Rivierenbuurt-noord 13
 
4.6%
Huygenspark 10
 
3.5%
Zuidwal 8
 
2.8%
Laakkwartier-oost 8
 
2.8%
Scheveningen Badplaats 7
 
2.5%
Bloemenbuurt-oost 6
 
2.1%
Other values (36) 83
29.4%
(Missing) 61
21.6%

Length

2025-09-09T16:37:52.942472image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
belgisch 25
 
9.3%
park 25
 
9.3%
bezuidenhout-oost 25
 
9.3%
statenkwartier 20
 
7.5%
bezuidenhout-midden 16
 
6.0%
rivierenbuurt-noord 13
 
4.9%
e.o 12
 
4.5%
huygenspark 10
 
3.7%
laakkwartier-oost 8
 
3.0%
zuidwal 8
 
3.0%
Other values (42) 106
39.6%

Most occurring characters

ValueCountFrequency (%)
e 384
 
11.5%
t 248
 
7.4%
r 239
 
7.2%
i 231
 
6.9%
o 227
 
6.8%
u 220
 
6.6%
n 218
 
6.5%
a 170
 
5.1%
d 144
 
4.3%
s 139
 
4.2%
Other values (33) 1112
33.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3332
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 384
 
11.5%
t 248
 
7.4%
r 239
 
7.2%
i 231
 
6.9%
o 227
 
6.8%
u 220
 
6.6%
n 218
 
6.5%
a 170
 
5.1%
d 144
 
4.3%
s 139
 
4.2%
Other values (33) 1112
33.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3332
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 384
 
11.5%
t 248
 
7.4%
r 239
 
7.2%
i 231
 
6.9%
o 227
 
6.8%
u 220
 
6.6%
n 218
 
6.5%
a 170
 
5.1%
d 144
 
4.3%
s 139
 
4.2%
Other values (33) 1112
33.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3332
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 384
 
11.5%
t 248
 
7.4%
r 239
 
7.2%
i 231
 
6.9%
o 227
 
6.8%
u 220
 
6.6%
n 218
 
6.5%
a 170
 
5.1%
d 144
 
4.3%
s 139
 
4.2%
Other values (33) 1112
33.4%

DISTRICT
Categorical

High correlation 

Distinct7
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
Haagse Hout
75 
Centrum
74 
Scheveningen
68 
Segbroek
41 
Laak
13 
Other values (2)
11 

Length

Max length12
Median length11
Mean length9.2943262
Min length4

Characters and Unicode

Total characters2621
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHaagse Hout
2nd rowHaagse Hout
3rd rowScheveningen
4th rowSegbroek
5th rowScheveningen

Common Values

ValueCountFrequency (%)
Haagse Hout 75
26.6%
Centrum 74
26.2%
Scheveningen 68
24.1%
Segbroek 41
14.5%
Laak 13
 
4.6%
Escamp 7
 
2.5%
Loosduinen 4
 
1.4%

Length

2025-09-09T16:37:53.034094image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-09T16:37:53.117049image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
haagse 75
21.0%
hout 75
21.0%
centrum 74
20.7%
scheveningen 68
19.0%
segbroek 41
11.5%
laak 13
 
3.6%
escamp 7
 
2.0%
loosduinen 4
 
1.1%

Most occurring characters

ValueCountFrequency (%)
e 439
16.7%
n 286
 
10.9%
g 184
 
7.0%
a 183
 
7.0%
u 153
 
5.8%
H 150
 
5.7%
t 149
 
5.7%
o 124
 
4.7%
r 115
 
4.4%
S 109
 
4.2%
Other values (14) 729
27.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2621
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 439
16.7%
n 286
 
10.9%
g 184
 
7.0%
a 183
 
7.0%
u 153
 
5.8%
H 150
 
5.7%
t 149
 
5.7%
o 124
 
4.7%
r 115
 
4.4%
S 109
 
4.2%
Other values (14) 729
27.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2621
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 439
16.7%
n 286
 
10.9%
g 184
 
7.0%
a 183
 
7.0%
u 153
 
5.8%
H 150
 
5.7%
t 149
 
5.7%
o 124
 
4.7%
r 115
 
4.4%
S 109
 
4.2%
Other values (14) 729
27.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2621
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 439
16.7%
n 286
 
10.9%
g 184
 
7.0%
a 183
 
7.0%
u 153
 
5.8%
H 150
 
5.7%
t 149
 
5.7%
o 124
 
4.7%
r 115
 
4.4%
S 109
 
4.2%
Other values (14) 729
27.8%

STATUS
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size17.5 KiB
Actief
281 
Non Actief
 
1

Length

Max length10
Median length6
Mean length6.0141844
Min length6

Characters and Unicode

Total characters1696
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st rowActief
2nd rowActief
3rd rowActief
4th rowActief
5th rowActief

Common Values

ValueCountFrequency (%)
Actief 281
99.6%
Non Actief 1
 
0.4%

Length

2025-09-09T16:37:53.211763image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-09T16:37:53.280367image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
actief 282
99.6%
non 1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
A 282
16.6%
c 282
16.6%
t 282
16.6%
i 282
16.6%
e 282
16.6%
f 282
16.6%
N 1
 
0.1%
o 1
 
0.1%
n 1
 
0.1%
1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1696
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 282
16.6%
c 282
16.6%
t 282
16.6%
i 282
16.6%
e 282
16.6%
f 282
16.6%
N 1
 
0.1%
o 1
 
0.1%
n 1
 
0.1%
1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1696
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 282
16.6%
c 282
16.6%
t 282
16.6%
i 282
16.6%
e 282
16.6%
f 282
16.6%
N 1
 
0.1%
o 1
 
0.1%
n 1
 
0.1%
1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1696
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 282
16.6%
c 282
16.6%
t 282
16.6%
i 282
16.6%
e 282
16.6%
f 282
16.6%
N 1
 
0.1%
o 1
 
0.1%
n 1
 
0.1%
1
 
0.1%
Distinct95
Distinct (%)33.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
Minimum2024-05-15 11:56:37
Maximum2025-04-30 09:26:41
Invalid dates0
Invalid dates (%)0.0%
2025-09-09T16:37:53.349947image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:53.443977image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

DISTRICT.1
Categorical

High correlation  Missing 

Distinct24
Distinct (%)10.9%
Missing61
Missing (%)21.6%
Memory size19.0 KiB
Bezuidenhout
44 
Stationsbuurt
26 
Belgisch Park
25 
Geuzen- en Statenkwartier
22 
Centrum
12 
Other values (19)
92 

Length

Max length25
Median length23
Mean length15.036199
Min length7

Characters and Unicode

Total characters3323
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)2.3%

Sample

1st rowBezuidenhout
2nd rowBezuidenhout
3rd rowScheveningen
4th rowVruchtenbuurt
5th rowBelgisch Park

Common Values

ValueCountFrequency (%)
Bezuidenhout 44
15.6%
Stationsbuurt 26
9.2%
Belgisch Park 25
8.9%
Geuzen- en Statenkwartier 22
 
7.8%
Centrum 12
 
4.3%
Scheveningen 10
 
3.5%
Schildersbuurt 10
 
3.5%
Benoordenhout 10
 
3.5%
Bomen- en Bloemenbuurt 10
 
3.5%
Laakkwartier en Spoorwijk 9
 
3.2%
Other values (14) 43
15.2%
(Missing) 61
21.6%

Length

2025-09-09T16:37:53.545762image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
en 49
14.2%
bezuidenhout 44
12.8%
stationsbuurt 26
 
7.6%
belgisch 25
 
7.3%
park 25
 
7.3%
geuzen 22
 
6.4%
statenkwartier 22
 
6.4%
centrum 12
 
3.5%
scheveningen 10
 
2.9%
schildersbuurt 10
 
2.9%
Other values (22) 99
28.8%

Most occurring characters

ValueCountFrequency (%)
e 459
13.8%
t 287
 
8.6%
n 282
 
8.5%
u 265
 
8.0%
r 248
 
7.5%
i 189
 
5.7%
o 167
 
5.0%
a 152
 
4.6%
123
 
3.7%
h 116
 
3.5%
Other values (30) 1035
31.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3323
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 459
13.8%
t 287
 
8.6%
n 282
 
8.5%
u 265
 
8.0%
r 248
 
7.5%
i 189
 
5.7%
o 167
 
5.0%
a 152
 
4.6%
123
 
3.7%
h 116
 
3.5%
Other values (30) 1035
31.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3323
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 459
13.8%
t 287
 
8.6%
n 282
 
8.5%
u 265
 
8.0%
r 248
 
7.5%
i 189
 
5.7%
o 167
 
5.0%
a 152
 
4.6%
123
 
3.7%
h 116
 
3.5%
Other values (30) 1035
31.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3323
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 459
13.8%
t 287
 
8.6%
n 282
 
8.5%
u 265
 
8.0%
r 248
 
7.5%
i 189
 
5.7%
o 167
 
5.0%
a 152
 
4.6%
123
 
3.7%
h 116
 
3.5%
Other values (30) 1035
31.1%

X
Real number (ℝ)

High correlation 

Distinct268
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80816.608
Minimum75569.44
Maximum84606.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2025-09-09T16:37:53.638176image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum75569.44
5-th percentile77686.592
Q179108.823
median81218.13
Q382394.23
95-th percentile83543.141
Maximum84606.62
Range9037.18
Interquartile range (IQR)3285.4075

Descriptive statistics

Standard deviation1942.0301
Coefficient of variation (CV)0.024030087
Kurtosis-0.95019059
Mean80816.608
Median Absolute Deviation (MAD)1589.305
Skewness-0.26347816
Sum22790283
Variance3771481.1
MonotonicityNot monotonic
2025-09-09T16:37:53.731064image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77852.92 3
 
1.1%
81416.35 2
 
0.7%
82458.1 2
 
0.7%
83409.26 2
 
0.7%
79115.97 2
 
0.7%
83730.04 2
 
0.7%
83556.17 2
 
0.7%
78682.5 2
 
0.7%
79106.44 2
 
0.7%
78192.1 2
 
0.7%
Other values (258) 261
92.6%
ValueCountFrequency (%)
75569.44 1
0.4%
75607.73 1
0.4%
76190.76 1
0.4%
76891.48 1
0.4%
76914.46 1
0.4%
77087.78 1
0.4%
77250.22 1
0.4%
77324.46 1
0.4%
77488.97 1
0.4%
77498.71 1
0.4%
ValueCountFrequency (%)
84606.62 1
0.4%
83861.74 1
0.4%
83824.6 1
0.4%
83785.09 1
0.4%
83730.04 2
0.7%
83664.33 1
0.4%
83635.36 1
0.4%
83607.45 1
0.4%
83595.84 1
0.4%
83588.42 1
0.4%

Y
Real number (ℝ)

High correlation 

Distinct266
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean455567.41
Minimum452079.34
Maximum459310.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2025-09-09T16:37:53.817082image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum452079.34
5-th percentile453144.78
Q1454497.7
median455316.93
Q3456354.29
95-th percentile458696.77
Maximum459310.66
Range7231.32
Interquartile range (IQR)1856.5875

Descriptive statistics

Standard deviation1591.056
Coefficient of variation (CV)0.003492471
Kurtosis-0.25335894
Mean455567.41
Median Absolute Deviation (MAD)930.895
Skewness0.53248686
Sum1.2847001 × 108
Variance2531459.1
MonotonicityNot monotonic
2025-09-09T16:37:53.911856image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
454867.71 3
 
1.1%
456309.77 2
 
0.7%
455615.79 2
 
0.7%
452769.66 2
 
0.7%
454603.11 2
 
0.7%
456012.21 2
 
0.7%
458120.33 2
 
0.7%
455714.26 2
 
0.7%
456045.86 2
 
0.7%
456350.46 2
 
0.7%
Other values (256) 261
92.6%
ValueCountFrequency (%)
452079.34 1
0.4%
452528.59 1
0.4%
452569.13 1
0.4%
452571.82 1
0.4%
452645.79 1
0.4%
452769.66 2
0.7%
452810.9 1
0.4%
452921.82 1
0.4%
452965.09 1
0.4%
452993.61 1
0.4%
ValueCountFrequency (%)
459310.66 1
0.4%
459111.31 1
0.4%
459098.07 1
0.4%
459082.28 1
0.4%
458822.44 1
0.4%
458807.02 1
0.4%
458800.08 1
0.4%
458766.55 1
0.4%
458764.13 1
0.4%
458760.72 1
0.4%

LONG
Real number (ℝ)

High correlation 

Distinct268
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3049037
Minimum4.2287099
Maximum4.3597774
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2025-09-09T16:37:54.006370image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum4.2287099
5-th percentile4.2595713
Q14.2795574
median4.3108716
Q34.3282401
95-th percentile4.3446469
Maximum4.3597774
Range0.1310675
Interquartile range (IQR)0.048682752

Descriptive statistics

Standard deviation0.028366397
Coefficient of variation (CV)0.006589322
Kurtosis-0.9827247
Mean4.3049037
Median Absolute Deviation (MAD)0.0237959
Skewness-0.25518508
Sum1213.9828
Variance0.00080465245
MonotonicityNot monotonic
2025-09-09T16:37:54.105546image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.261832002 3
 
1.1%
4.313861111 2
 
0.7%
4.329443884 2
 
0.7%
4.342568152 2
 
0.7%
4.279529245 2
 
0.7%
4.347372611 2
 
0.7%
4.34476678 2
 
0.7%
4.273599395 2
 
0.7%
4.279641847 2
 
0.7%
4.266380903 2
 
0.7%
Other values (258) 261
92.6%
ValueCountFrequency (%)
4.22870989 1
0.4%
4.229261542 1
0.4%
4.238233713 1
0.4%
4.248058164 1
0.4%
4.248384056 1
0.4%
4.250841945 1
0.4%
4.253214733 1
0.4%
4.254301201 1
0.4%
4.256375721 1
0.4%
4.256998791 1
0.4%
ValueCountFrequency (%)
4.359777389 1
0.4%
4.349287516 1
0.4%
4.348772574 1
0.4%
4.34819176 1
0.4%
4.347372611 2
0.7%
4.346348946 1
0.4%
4.346039505 1
0.4%
4.345601984 1
0.4%
4.345437845 1
0.4%
4.345323905 1
0.4%

LAT
Real number (ℝ)

High correlation 

Distinct268
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.083396
Minimum52.051414
Maximum52.116881
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2025-09-09T16:37:54.272820image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum52.051414
5-th percentile52.061788
Q152.073888
median52.081468
Q352.090555
95-th percentile52.111407
Maximum52.116881
Range0.065466468
Interquartile range (IQR)0.016667942

Descriptive statistics

Standard deviation0.014273121
Coefficient of variation (CV)0.00027404359
Kurtosis-0.26606068
Mean52.083396
Median Absolute Deviation (MAD)0.0083871797
Skewness0.5172674
Sum14687.518
Variance0.00020372198
MonotonicityNot monotonic
2025-09-09T16:37:54.367467image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52.07670649 3
 
1.1%
52.07481363 2
 
0.7%
52.05847415 2
 
0.7%
52.09041231 2
 
0.7%
52.10611127 2
 
0.7%
52.08510194 2
 
0.7%
52.08805957 2
 
0.7%
52.09014645 2
 
0.7%
52.09595505 2
 
0.7%
52.09263189 2
 
0.7%
Other values (258) 261
92.6%
ValueCountFrequency (%)
52.05141435 1
0.4%
52.05603091 1
0.4%
52.05659596 1
0.4%
52.05663445 1
0.4%
52.05735349 1
0.4%
52.05847415 2
0.7%
52.0588117 1
0.4%
52.05937609 1
0.4%
52.05993263 1
0.4%
52.06023487 1
0.4%
ValueCountFrequency (%)
52.11688082 1
0.4%
52.11508628 1
0.4%
52.11497682 1
0.4%
52.11482933 1
0.4%
52.11251706 1
0.4%
52.11237162 1
0.4%
52.11230586 1
0.4%
52.11201806 1
0.4%
52.11199593 1
0.4%
52.11199371 1
0.4%

Interactions

2025-09-09T16:37:49.279353image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:45.771122image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:46.302810image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:46.779820image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:47.284739image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:47.768336image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:48.295397image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:48.791635image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:49.338450image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:45.828192image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:46.357492image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:46.841113image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:47.342057image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:47.891613image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:48.354105image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:48.848324image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:49.399107image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:45.938658image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:46.412621image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:46.901716image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:47.398866image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:47.948277image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:48.415602image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:48.909188image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:49.466726image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:46.003444image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:46.474770image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:46.973254image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:47.465274image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:48.009172image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:48.480217image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:48.973839image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:49.527380image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:46.063751image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:46.533861image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:47.034054image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:47.523395image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:48.066036image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:48.543365image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:49.034515image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:49.585117image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:46.117570image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:46.587427image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:47.091369image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:47.578502image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:48.117673image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:48.600512image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:49.089598image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:49.651151image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:46.179872image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:46.649613image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:47.156659image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:47.641835image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:48.177214image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:48.664553image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:49.155550image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:49.718044image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:46.237681image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:46.713088image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:47.219449image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:47.704039image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:48.235606image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:48.726220image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-09T16:37:49.214670image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-09-09T16:37:54.439852image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
DIRECTIONDISTRICTDISTRICT.1IDLATLONGNEIGHBOURHOODNUMBERSEQ NUMBERSTATUSXYYEAR_PLACEMENT
DIRECTION1.0000.1670.0390.7050.1070.1460.4960.0000.6321.0000.1430.0810.378
DISTRICT0.1671.0000.9590.2490.5440.6340.9040.0000.1480.2290.6290.5350.168
DISTRICT.10.0390.9591.0001.0000.6520.6750.9430.0000.2690.0500.6620.6560.233
ID0.7050.2491.0001.000-0.1840.0311.000-0.0030.9980.0000.032-0.1830.694
LAT0.1070.5440.652-0.1841.000-0.0240.6820.055-0.1830.407-0.0171.000-0.151
LONG0.1460.6340.6750.031-0.0241.0000.7630.0390.0300.0001.000-0.0400.014
NEIGHBOURHOOD0.4960.9040.9431.0000.6820.7631.0000.0000.3090.0000.7520.6730.269
NUMBER0.0000.0000.000-0.0030.0550.0390.0001.000-0.0040.0000.0400.0530.060
SEQ NUMBER0.6320.1480.2690.998-0.1830.0300.309-0.0041.0000.0160.030-0.1820.696
STATUS1.0000.2290.0500.0000.4070.0000.0000.0000.0161.0000.0000.3290.000
X0.1430.6290.6620.032-0.0171.0000.7520.0400.0300.0001.000-0.0330.014
Y0.0810.5350.656-0.1831.000-0.0400.6730.053-0.1820.329-0.0331.000-0.151
YEAR_PLACEMENT0.3780.1680.2330.694-0.1510.0140.2690.0600.6960.0000.014-0.1511.000

Missing values

2025-09-09T16:37:49.904864image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-09T16:37:50.097531image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-09-09T16:37:50.225802image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IDMANAGEMENT AREASEQ NUMBERHOUSE NUMBERDESCRIPTIONPHOTO FILETEXT_ON_THE_STONENAMES_OF THOSE INVOLVEDNUMBERYEAR_PLACEMENTBWT_NUMMERDIRECTIONDATE_ACQUISITIONNEIGHBOURHOODDISTRICTSTATUSLASTUPDATEDISTRICT.1XYLONGLAT
06009918Juliana van Stolberglaan88352NaNhttps://denhaag.gisib.nl/fotos/Stolpersteine/Foto/88.jpgNaNBenjamin van Dantzig (joodsmonument.nl)22013.0BWT-01373GGB12Haaks2023-11-01Bezuidenhout-oostHaagse HoutActief2024-05-15 11:56:37Bezuidenhout83588.42455632.094.34532452.084345
16009919Juliana van Stolberglaan89368NaNhttps://denhaag.gisib.nl/fotos/Stolpersteine/Foto/89.jpgNaNGerson de Levie (joodsmonument.nl)12013.0BWT-00109GGB13Haaks2023-11-01Bezuidenhout-oostHaagse HoutActief2024-05-15 11:56:37Bezuidenhout83607.45455629.804.34560252.084327
26009920Keizerstraat9065NaNhttps://denhaag.gisib.nl/fotos/Stolpersteine/Foto/90.jpgNaNFrederik Alter (joodsmonument.nl)32018.0BWT-00185GGB17Haaks2023-11-01Scheveningen BadplaatsScheveningenActief2024-05-15 11:56:37Scheveningen78711.53458131.404.27362452.106155
36009921Kornoeljestraat914NaNhttps://denhaag.gisib.nl/fotos/Stolpersteine/Foto/91.jpgNaNJudica Mozes (joodsmonument.nl)12022.0BWT-00274GGB20Haaks2023-11-01EykenduinenSegbroekActief2024-05-15 11:56:37Vruchtenbuurt77695.36453555.914.25983152.064896
46009922Kortrijksestraat9241NaNhttps://denhaag.gisib.nl/fotos/Stolpersteine/Foto/92.jpgNaNHans Paradies (joodsmonument.nl)42022.0BWT-00299GGB19Haaks2023-11-01Belgisch ParkScheveningenActief2024-05-15 11:56:37Belgisch Park80212.48458548.984.29543852.110113
56009923Kraijenhoffstraat9352-54NaNhttps://denhaag.gisib.nl/fotos/Stolpersteine/Foto/93.jpgNaNElias Viskoper (joodsmonument.nl)22023.0BWT-01284GGB22Haaks2023-11-01HuygensparkCentrumActief2024-05-15 11:56:37Stationsbuurt81844.70454143.364.32020752.070739
66009924Laakkade9494NaNhttps://denhaag.gisib.nl/fotos/Stolpersteine/Foto/94.jpgNaNArthur Levin (joodsmonument.nl)42023.0BWT-00001GGB22Haaks2023-11-01Laakkwartier-oostLaakActief2024-05-15 11:56:37Laakkwartier en Spoorwijk82410.12453591.434.32857052.065853
76009925Laan9520 /26NaNhttps://denhaag.gisib.nl/fotos/Stolpersteine/Foto/95.jpgNaNAdolf Mendels (joodsmonument.nl)32018.0NaNHaaks2023-11-01KortenbosCentrumActief2024-05-15 11:56:37Centrum80941.70454689.194.30692052.075524
86009926Laan Copes van Cattenburch96129NaNhttps://denhaag.gisib.nl/fotos/Stolpersteine/Foto/96.jpgNaNHerman Salomonson (joodsmonument.nl)22019.0BWT-00732GGB18Haaks2023-11-01ArchipelbuurtCentrumActief2024-05-15 11:56:37Archipelbuurt81028.85456490.184.30780052.091721
96009870De Vriesstraat3925NaNhttps://denhaag.gisib.nl/fotos/Stolpersteine/Foto/39.jpgNaNAndries Jonas van Rood (joodsmonument.nl)22022.0BWT-00890GGB20Haaks2023-11-01Bezuidenhout-oostHaagse HoutActief2024-05-15 11:56:37Bezuidenhout83477.34455506.664.34373052.083204
IDMANAGEMENT AREASEQ NUMBERHOUSE NUMBERDESCRIPTIONPHOTO FILETEXT_ON_THE_STONENAMES_OF THOSE INVOLVEDNUMBERYEAR_PLACEMENTBWT_NUMMERDIRECTIONDATE_ACQUISITIONNEIGHBOURHOODDISTRICTSTATUSLASTUPDATEDISTRICT.1XYLONGLAT
2726905007Carel Reinierszkade276259NaNhttps://denhaag.gisib.nl/fotos/Stolpersteine/Foto/276.jpgNaNNathan de Wit22025.0BWT-00170GGB24haaks2025-04-24NaNHaagse HoutActief2025-04-28 14:37:39NaN83861.74455744.454.34928852.085390
2736905008Van Alkemadelaan27743NaNhttps://denhaag.gisib.nl/fotos/Stolpersteine/Foto/277.jpgNaNLiza Kan-van Zwanenberg22025.0BWT-00168GGB24haaks2025-04-24NaNHaagse HoutActief2025-04-28 14:37:07NaN82471.40456752.724.32879152.094271
2746905009Van Heutszstraat27896NaNhttps://denhaag.gisib.nl/fotos/Stolpersteine/Foto/278.jpgNaNJosef Barchasch52025.0BWT-02175GGB23haaks2025-04-24NaNHaagse HoutActief2025-04-28 14:34:00NaN83785.09455637.174.34819252.084416
2756905010Prinses Marijkestraat279385NaNhttps://denhaag.gisib.nl/fotos/Stolpersteine/Foto/279.jpgNaNLuc Sassenus22025.0BWT-01262GGB24haaks2025-04-24NaNHaagse HoutActief2025-04-28 14:32:28NaN82979.13455221.024.33652352.080572
2766905011Van Soutelandelaan28045NaNhttps://denhaag.gisib.nl/fotos/Stolpersteine/Foto/280.jpgNaNSimon Tobias van Blankenstein12025.0BWT-01113GGB23haaks2025-04-24NaNHaagse HoutActief2025-04-28 14:29:29NaN81822.44457473.334.31916852.100661
2776905012De Mildestraat28111NaNhttps://denhaag.gisib.nl/fotos/Stolpersteine/Foto/281.jpgNaNDavid van Hessen42025.0BWT-02823GGB23haaks2025-04-24NaNHaagse HoutActief2025-04-28 14:28:43NaN81967.72457101.974.32136752.097343
2786905013Spui282191NaNhttps://denhaag.gisib.nl/fotos/Stolpersteine/Foto/282.jpgNaNLeonardus Josephus Maria 'Leo'Coppes22025.0BWT-01048GGB24haaks2025-04-24NaNCentrumActief2025-04-28 14:27:51NaN81645.34454761.194.31716752.076265
2796905014Paviljoensgracht28337NaNhttps://denhaag.gisib.nl/fotos/Stolpersteine/Foto/283.jpgNaNBenjamin Mozes Levits22025.0BWT-01272GGB20haaks2025-04-24NaNCentrumActief2025-04-28 14:26:50NaN81323.30454497.614.31252752.073853
2806905015Stille Veerkade2841NaNhttps://denhaag.gisib.nl/fotos/Stolpersteine/Foto/284.jpgNaNLeon Tokkie32025.0BWT-02177GG23haaks2025-04-24NaNCentrumActief2025-04-28 14:23:33NaN81470.97454560.934.31466752.074442
2816905016Van Limburg Stirumstraat2856NaNhttps://denhaag.gisib.nl/fotos/Stolpersteine/Foto/285.jpgNaNEster Bobbe12025.0BWT-01292GGB24haaks2025-04-24NaNCentrumActief2025-04-28 14:21:44NaN81816.55454279.764.31976752.071961